Introduces the basic concepts and methods of statistics as applied to diverse problems in public health and medicine. Demonstrates methods of exploring, organizing, and presenting data, and introduces fundamentals of probability, including probability distributions and conditional probability, with applications to 2x2 tables. Presents the foundations of statistical inference, including concepts of population, sample parameter, and estimate; and approaches to inferences using the likelihood function, confidence intervals, and hypothesis tests. Introduces and employs the statistical computing package, STATA, to manipulate data and prepare students for remaining course work in this sequence.

Learning Objectives:

Upon successfully completing this course, students will be able to:

Use statistical reasoning to formulate public health questions in quantitative terms [1.1 Discuss the role of statistical reasoning within the scientific method. 1.2 Discuss and apply the counterfactual definition of causal effects in public health

Use probability models to describe trends and random variation in public health data 3.1 Use the statistical analysis package Stata to make basic statistical computations in combination with graphical displays; 3.2 Use the concepts of probability to describe the effect of a treatment on a health outcome in a randomized trial; 3.3 Use the binomial distribution and the Poisson approximation to the binomial to calculate probabilities of events; 3.4 Use the Gaussian or normal probability model to approximate the distribution of a continuous public health measure and assess the quality of this approximation; 3.5 Generate and interpret a quantile-quantile (Q-Q) plot to compare two distributions

Use statistical methods for inference, including tests and confidence intervals, to draw public health inferences from data 4.1 Generate random numbers and appreciate the sources of variation among multiple observations of a random process; 4.2 Explain the implications of the Central Limit Theorem in determining the sampling distribution of the mean of n observations; 4.3 Use bootstrapping to determine confidence intervals and interpret them in a scientific context; 4.4 Use sampling distribution theory for the mean and for differences between two means to create confidence intervals and hypothesis tests; 4.5 Use stratification to eliminate the influence of a possible confounding variable in a study of the association of a risk factor and outcome; 4.6 Construct and interpret the appropriate two-sample confidence interval and t-test to assess whether average outcome is different between two groups; 4.7 Use the paired-sample t-test and confidence intervals